Avisha Das

425 total citations
13 papers, 228 citations indexed

About

Avisha Das is a scholar working on Artificial Intelligence, Information Systems and Molecular Biology. According to data from OpenAlex, Avisha Das has authored 13 papers receiving a total of 228 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 4 papers in Information Systems and 3 papers in Molecular Biology. Recurrent topics in Avisha Das's work include Topic Modeling (9 papers), Spam and Phishing Detection (3 papers) and Biomedical Text Mining and Ontologies (3 papers). Avisha Das is often cited by papers focused on Topic Modeling (9 papers), Spam and Phishing Detection (3 papers) and Biomedical Text Mining and Ontologies (3 papers). Avisha Das collaborates with scholars based in United States and Iran. Avisha Das's co-authors include Rakesh Verma, Hua Xu, Jianfu Li, Vipina K. Keloth, Xu Zuo, Salih Selek, Azadeh Shakery, Yan Hu, Cui Tao and Liang‐Chin Huang and has published in prestigious journals such as IEEE Access, American Journal of Roentgenology and Journal of the American Medical Informatics Association.

In The Last Decade

Avisha Das

11 papers receiving 208 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Avisha Das United States 7 150 120 88 61 30 13 228
Ekaterina Kochmar United Kingdom 9 68 0.5× 260 2.2× 23 0.3× 21 0.3× 37 1.2× 32 331
Lena Wiese Germany 8 54 0.4× 84 0.7× 14 0.2× 57 0.9× 11 0.4× 40 166
Mohd Fazil India 9 159 1.1× 118 1.0× 81 0.9× 84 1.4× 89 3.0× 27 267
Aliaksandr Barushka Czechia 5 190 1.3× 191 1.6× 35 0.4× 66 1.1× 73 2.4× 7 259
Anja Lehmann Switzerland 9 90 0.6× 135 1.1× 20 0.2× 44 0.7× 35 1.2× 31 204
Tommi Gröndahl Finland 5 99 0.7× 162 1.4× 58 0.7× 20 0.3× 36 1.2× 9 212
Katrina E. Triezenberg United States 7 84 0.6× 56 0.5× 42 0.5× 59 1.0× 19 0.6× 10 149
Georgia Frantzeskou Greece 6 192 1.3× 197 1.6× 78 0.9× 25 0.4× 17 0.6× 7 267
Chaoyi Lu China 9 100 0.7× 165 1.4× 61 0.7× 141 2.3× 22 0.7× 21 245
Fergus Toolan Ireland 6 164 1.1× 124 1.0× 63 0.7× 58 1.0× 28 0.9× 9 204

Countries citing papers authored by Avisha Das

Since Specialization
Citations

This map shows the geographic impact of Avisha Das's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Avisha Das with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Avisha Das more than expected).

Fields of papers citing papers by Avisha Das

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Avisha Das. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Avisha Das. The network helps show where Avisha Das may publish in the future.

Co-authorship network of co-authors of Avisha Das

This figure shows the co-authorship network connecting the top 25 collaborators of Avisha Das. A scholar is included among the top collaborators of Avisha Das based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Avisha Das. Avisha Das is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

13 of 13 papers shown
1.
Das, Avisha, et al.. (2025). Out-of-the-Box Large Language Models for Detecting and Classifying Critical Findings in Radiology Reports Using Various Prompt Strategies. American Journal of Roentgenology. 225(6). e2533469–e2533469.
2.
Das, Avisha, et al.. (2025). Weakly supervised language models for automated extraction of critical findings from radiology reports. npj Digital Medicine. 8(1). 257–257. 3 indexed citations
3.
Das, Avisha, et al.. (2025). Efficient Training Corpus Retrieval for Large Language Model Fine Tuning: A Case Study in Cancer. Studies in health technology and informatics. 329. 1251–1255.
4.
Wei, Qiang, Liang‐Chin Huang, Jianfu Li, et al.. (2024). Ensemble pretrained language models to extract biomedical knowledge from literature. Journal of the American Medical Informatics Association. 31(9). 1904–1911. 11 indexed citations
5.
Das, Avisha, et al.. (2023). Representation Learning of Biological Concepts: A Systematic Review. Current Bioinformatics. 19(1). 61–72. 1 indexed citations
6.
Das, Avisha, Salih Selek, Xu Zuo, et al.. (2022). Conversational Bots for Psychotherapy: A Study of Generative Transformer Models Using Domain-specific Dialogues. 285–297. 25 indexed citations
7.
Verma, Rakesh, et al.. (2020). Diverse Datasets and a Customizable Benchmarking Framework for Phishing. 35–41. 13 indexed citations
8.
Das, Avisha & Rakesh Verma. (2020). Can Machines Tell Stories? A Comparative Study of Deep Neural Language Models and Metrics. IEEE Access. 8. 181258–181292. 17 indexed citations
9.
Das, Avisha, et al.. (2020). An In-Depth Benchmarking and Evaluation of Phishing Detection Research for Security Needs. IEEE Access. 8. 22170–22192. 91 indexed citations
10.
Das, Avisha, et al.. (2018). University of Houston @ CL-SciSumm 2018.. International ACM SIGIR Conference on Research and Development in Information Retrieval. 142–149. 3 indexed citations
11.
Das, Avisha, et al.. (2018). Citance-based retrieval and summarization using IR and machine learning. Scientometrics. 116(2). 1331–1366. 10 indexed citations
12.
Das, Avisha, et al.. (2017). University of Houston @ CL-SciSumm 2017: Positional language Models, Structural Correspondence Learning and Textual Entailment.. International ACM SIGIR Conference on Research and Development in Information Retrieval. 73–85. 4 indexed citations
13.
Verma, Rakesh & Avisha Das. (2017). What's in a URL. 55–63. 50 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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